Coronary artery disease (CAD) is one of the most common causes of death in
the European Union and the USA. The crucial biomarker in its diagnosis is
called Fractional Flow Reserve (FFR) and its in-vivo measurement is obtained
via an invasive diagnostic technique in the form of coronagraphy. In order to
address the invasive drawbacks associated with a procedure, a new approach
virtual FFR (vFFR) measurement has emerged in recent years. This technique
involves using computed tomography angiography (CTA) to obtain virtual
measurements of FFR. By utilizing Computational Fluid Dynamics (CFD), vFFR
estimates can be derived from CTA data, providing a promising in-silico
alternative to traditional methods. However, the widespread adoption of vFFR
from CTA as a diagnostic technique is hindered by two main challenges: time and
computational requirements. In this work, we explore the usage of deep learning
techniques as surrogate CFD engine models in the task of vFFR estimation in
coronary arteries to drastically limit the required time and computational
costs without a major drop in quality. We propose a novel approach to vFFR
estimation by representing the input vessel geometry as a point cloud and
utilizing the hybrid neural network that learns geometry representation based
on both explicitly and implicitly given features. We evaluate the method from
the clinical point of view and showcase that it can serve as a compelling
replacement for commonly utilized CFD-based approaches.

冠状动脉疾病是欧盟和美国最常见的死因之一，其中关键的生物标志物是分数流量减少 (FFR)，通过侵入性诊断技术冠状动脉造影获得其体内测量。为了解决侵入性的缺点，近年来出现了一种名为虚拟 FFR (vFFR) 测量的新方法。本文中，我们探讨了使用深度学习技术作为替代 CFD 引擎模型在冠状动脉 vFFR 估计中的任务中，以极大地减少所需时间和计算成本而不会显著降低质量的方法。我们提出了一种新颖的 vFFR 估计方法，通过将输入血管几何表示为点云，并利用学习几何表示的混合神经网络，基于明确和隐式给定的特征。我们从临床角度评估了该方法，并展示它可以作为常用的基于 CFD 的方法的有力替代。

使用深度学习估计冠状动脉中的 FFR

Estimation of FFR in coronary arteries with deep learning

Functionally significant coronary artery disease (CAD) is caused by plaque
buildup in the coronary arteries, potentially leading to narrowing of the
arterial lumen, i.e. coronary stenosis, that significantly obstructs blood flow
to the myocardium. The current reference for establishing the presence of a
functionally significant stenosis is invasive fractional flow reserve (FFR)
measurement. To avoid invasive measurements, non-invasive prediction of FFR
from coronary CT angiography (CCTA) has emerged. For this, machine learning
approaches, characterized by fast inference, are increasingly developed.
However, these methods predict a single FFR value per artery i.e. they don't
provide information about the stenosis location or treatment strategy. We
propose a deep learning-based method to predict the FFR along the artery from
CCTA scans. This study includes CCTA images of 110 patients who underwent
invasive FFR pullback measurement in 112 arteries. First, a multi planar
reconstruction (MPR) of the artery is fed to a variational autoencoder to
characterize the artery, i.e. through the lumen area and unsupervised artery
encodings. Thereafter, a convolutional neural network (CNN) predicts the FFR
along the artery. The CNN is supervised by multiple loss functions, notably a
loss function inspired by the Earth Mover's Distance (EMD) to predict the
correct location of FFR drops and a histogram-based loss to explicitly
supervise the slope of the FFR curve. To train and evaluate our model,
eight-fold cross-validation was performed. The resulting FFR curves show good
agreement with the reference allowing the distinction between diffuse and focal
CAD distributions in most cases. Quantitative evaluation yielded a mean
absolute difference in the area under the FFR pullback curve (AUPC) of 1.7. The
method may pave the way towards fast, accurate, automatic prediction of FFR
along the artery from CCTA.

基于深度学习的方法从冠状动脉 CTA 扫描中预测冠状动脉沿程血流储备指数（FFR），为快速准确、自动化的 FFR 预测铺平了道路。

基于深度学习的冠状动脉分数流预测

Deep Learning-Based Prediction of Fractional Flow Reserve along the  Coronary Artery

Local hemodynamic forces play an important role in determining the functional
significance of coronary arterial stenosis and understanding the mechanism of
coronary disease progression. Computational fluid dynamics (CFD) have been
widely performed to simulate hemodynamics non-invasively from coronary computed
tomography angiography (CCTA) images. However, accurate computational analysis
is still limited by the complex construction of patient-specific modeling and
time-consuming computation. In this work, we proposed an end-to-end deep
learning framework, which could predict the coronary artery hemodynamics from
CCTA images. The model was trained on the hemodynamic data obtained from 3D
simulations of synthetic and real datasets. Extensive experiments demonstrated
that the predicted hemdynamic distributions by our method agreed well with the
CFD-derived results. Quantitatively, the proposed method has the capability of
predicting the fractional flow reserve with an average error of 0.5\% and 2.5\%
for the synthetic dataset and real dataset, respectively. Particularly, our
method achieved much better accuracy for the real dataset compared to
PointNet++ with the point cloud input. This study demonstrates the feasibility
and great potential of our end-to-end deep learning method as a fast and
accurate approach for hemodynamic analysis.

本文提出了一种基于深度学习的框架，利用计算机辅助技术快速准确地进行冠状动脉血液动力学分析， 通过迭代学习算法可以用 CCTA 图像预测冠状动脉的血液动力学分布，并与计算流体动力学分析结果相吻合，为冠状动脉疾病发展的机制揭示提供新思路。